Classification and morphometric features of pterion in thai population with potential sex prediction
Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmark...
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th-mahidol.776982022-08-04T16:07:34Z Classification and morphometric features of pterion in thai population with potential sex prediction Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago Faculty of Medicine, Khon Kaen University Mahidol University Medicine Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age. 2022-08-04T09:07:34Z 2022-08-04T09:07:34Z 2021-11-01 Article Medicina (Lithuania). Vol.57, No.11 (2021) 10.3390/medicina57111282 16489144 1010660X 2-s2.0-85120586172 https://repository.li.mahidol.ac.th/handle/123456789/77698 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85120586172&origin=inward |
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Medicine Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago Classification and morphometric features of pterion in thai population with potential sex prediction |
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Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age. |
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Faculty of Medicine, Khon Kaen University |
author_facet |
Faculty of Medicine, Khon Kaen University Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago |
format |
Article |
author |
Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago |
author_sort |
Nongnut Uabundit |
title |
Classification and morphometric features of pterion in thai population with potential sex prediction |
title_short |
Classification and morphometric features of pterion in thai population with potential sex prediction |
title_full |
Classification and morphometric features of pterion in thai population with potential sex prediction |
title_fullStr |
Classification and morphometric features of pterion in thai population with potential sex prediction |
title_full_unstemmed |
Classification and morphometric features of pterion in thai population with potential sex prediction |
title_sort |
classification and morphometric features of pterion in thai population with potential sex prediction |
publishDate |
2022 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/77698 |
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1763490949531435008 |